Gastos Casa
01 agosto, 2022
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(.,format = "html", format.args = list(decimal.mark = ",", big.mark = "."),
caption="Tabla 1. Gastos Casa (últimos 30 registros)", align =rep('c', 3)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
kableExtra::scroll_box(width = "100%", height = "300px")| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 24/6/2022 | Comida | 40.400 | Andrés | Bar la Providencia |
| 27/6/2022 | Agua | 12.502 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 29/6/2022 | Netflix | 8.320 | Tami | NA |
| 29/6/2022 | Comida | 68.213 | Tami | NA |
| 30/6/2022 | Comida | 15.310 | Tami | NA |
| 30/6/2022 | Electricidad | 67.655 | Andrés | NA |
| 2/7/2022 | Diosi | 35.990 | Andrés | NA |
| 3/7/2022 | Gas | 19.600 | Andrés | NA |
| 3/7/2022 | Parafina | 44.029 | Tami | NA |
| 11/7/2022 | Diosi | 15.930 | Tami | NA |
| 11/7/2022 | Comida | 60.660 | Tami | NA |
| 14/7/2022 | Enceres | 18.990 | Andrés | NA |
| 15/7/2022 | Ropa | 18.990 | Andrés | NA |
| 15/7/2022 | Ropa | 18.990 | Andrés | NA |
| 15/7/2022 | Comida | 15.000 | Andrés | NA |
| 19/7/2022 | Parafina | 22.521 | Tami | NA |
| 20/7/2022 | VTR | 21.990 | Andrés | NA |
| 21/7/2022 | Comida | 24.660 | Andrés | NA |
| 23/7/2022 | Enceres | 14.315 | Andrés | NA |
| 23/7/2022 | Comida | 22.263 | Andrés | NA |
| 20/7/2022 | Comida | 41.830 | Andrés | NA |
| 25/7/2022 | Comida | 61.470 | Tami | NA |
| 25/7/2022 | Comida | 16.100 | Tami | NA |
| 25/7/2022 | Cortina baño | 29.120 | Tami | NA |
| 28/7/2022 | Electricidad | 78.798 | Andrés | NA |
| 29/7/2022 | Netflix | 8.320 | Tami | NA |
| 30/7/2022 | Comida | 36.170 | Tami | NA |
| 31/7/2022 | Parafina | 22.060 | Tami | NA |
| 31/3/2019 | Comida | 9.000 | Andrés | NA |
| 8/9/2019 | Comida | 24.588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 4.2487e+08 2 4.7379 0.0092 **
## lag_depvar 7.3897e+10 1 1648.1010 <2e-16 ***
## Residuals 2.1163e+10 472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 895.069 13562.61 0.0205774
## 2-0 27953.942 22094.712 33813.17 0.0000000
## 2-1 20725.104 17157.287 24292.92 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 320 50188.20 16190.539
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2066.949065 4060.967382 -558.480784 2420.378865 -3009.989201
## 7 8 9 10 11
## 504.077230 -5677.415860 -1163.346693 -3939.124757 -365.267897
## 12 13 14 15 16
## -4894.522593 -1532.587590 -823.408525 447.423496 -3189.288798
## 17 18 19 20 21
## -308.054654 -2070.229082 6670.083691 -1531.868115 -1202.133048
## 22 23 24 25 26
## 1486.790158 -1193.719051 234.045092 1688.103390 -7126.769396
## 27 28 29 30 31
## 981.653833 8209.926232 360.105139 -72.555803 -2455.908522
## 32 33 34 35 36
## 1543.853629 4526.655832 1043.985149 2305.212900 -1967.791277
## 37 38 39 40 41
## 4532.250564 4259.123340 -2351.092144 -3028.426290 -1126.369508
## 42 43 44 45 46
## -10746.604402 7375.212047 2570.324379 1356.296662 8084.040966
## 47 48 49 50 51
## 598.883493 6446.362067 6586.909390 -6050.783372 -4893.279677
## 52 53 54 55 56
## -5105.050884 -7925.765445 6198.895344 -4068.390761 -4853.068343
## 57 58 59 60 61
## 3932.836363 922.185941 -9.977084 161.466547 -4981.193243
## 62 63 64 65 66
## 18181.941928 3535.363027 -3769.207340 5848.299504 7226.369962
## 67 68 69 70 71
## 14473.778875 1425.059304 -13461.537206 -1412.092178 4561.739339
## 72 73 74 75 76
## -5011.242091 -4460.304214 -10509.148255 2545.363554 -5352.171594
## 77 78 79 80 81
## 1150.915936 -6799.322827 663.401542 -2257.639122 -2589.417837
## 82 83 84 85 86
## -3817.979741 -404.859169 2434.861082 3847.817139 518.999125
## 87 88 89 90 91
## -452.211188 228.579994 4327.812946 -1177.288815 1147.870375
## 92 93 94 95 96
## -2077.205003 -1038.261129 191.362163 284.960662 -7477.833849
## 97 98 99 100 101
## 2460.453940 -8563.057048 -2833.175145 -3920.712137 -1598.677029
## 102 103 104 105 106
## -1126.006367 3309.826299 -2256.736831 2688.216698 -1098.462413
## 107 108 109 110 111
## 1032.384080 2632.628732 -3136.939687 -4681.293436 -773.877132
## 112 113 114 115 116
## 1977.229270 11741.464290 -1300.522638 2628.150436 4204.562933
## 117 118 119 120 121
## 3415.227569 -1206.404349 -4800.261054 -3757.574075 2321.946297
## 122 123 124 125 126
## -1750.729606 1339.107549 8845.436367 760.317513 47.148123
## 127 128 129 130 131
## -2595.428873 2611.437302 6991.552828 898.927368 -8607.479163
## 132 133 134 135 136
## 1726.738584 4100.710279 -3229.833867 -1450.696537 -869.257302
## 137 138 139 140 141
## -3886.387731 1210.185592 -482.112437 -2898.006471 1756.279574
## 142 143 144 145 146
## -1862.693408 -7797.544652 2133.500692 -3415.211192 2187.846678
## 147 148 149 150 151
## -200.918334 1074.260307 -323.617036 1386.056062 1204.341617
## 152 153 154 155 156
## 3361.584041 -4886.337844 -1154.903663 -3209.058681 6007.287947
## 157 158 159 160 161
## 9739.796890 -3131.806807 -4461.765421 3946.512733 488.439554
## 162 163 164 165 166
## 2976.065597 -5664.299015 -6456.865049 4493.325574 17675.382173
## 167 168 169 170 171
## 3742.402049 -307.284104 -2339.230064 -965.601100 3744.880759
## 172 173 174 175 176
## -101.872964 -7939.636732 3077.493450 4510.031573 771.397812
## 177 178 179 180 181
## 8895.416454 -9177.011996 -3303.586012 -10543.599494 -10945.468900
## 182 183 184 185 186
## 1615.816390 9638.179149 -1189.064859 6174.647054 6738.772417
## 187 188 189 190 191
## 13279.904050 8436.492816 -4117.062848 2472.743493 10370.946579
## 192 193 194 195 196
## -1719.367192 -2477.712467 -10268.611750 -6238.728567 1421.271254
## 197 198 199 200 201
## -5060.127115 -9575.342081 5688.748235 -2829.396588 -1453.494871
## 202 203 204 205 206
## -540.269530 6753.889178 10067.637028 665.516324 3014.043762
## 207 208 209 210 211
## 3167.416932 5834.921057 12842.399001 -5781.801674 -11305.724120
## 212 213 214 215 216
## -5548.087696 -10409.697189 -4798.565482 1838.423118 -12730.519845
## 217 218 219 220 221
## 16779.100182 8016.128885 1654.558907 26803.781108 12390.406009
## 222 223 224 225 226
## 7113.592763 13780.332992 -4245.643436 -1976.203108 3606.811913
## 227 228 229 230 231
## 194.202480 2617.872954 8887.334639 5664.667265 -2083.103897
## 232 233 234 235 236
## -1942.781002 9364.001482 -11630.745466 -7261.035929 -8432.196017
## 237 238 239 240 241
## -9904.780627 3366.096562 1596.561212 -8074.842240 -8695.191126
## 242 243 244 245 246
## 9459.009407 -7511.981830 2803.936545 -10026.830283 -3694.785565
## 247 248 249 250 251
## 1796.577388 1337.317349 -12012.522660 4047.129999 2401.271422
## 252 253 254 255 256
## 4508.413751 2373.416645 -952.932919 11351.276647 20976.773741
## 257 258 259 260 261
## 3092.921433 -4377.709794 4074.791363 -1747.999048 3724.422775
## 262 263 264 265 266
## -4881.890454 -10854.736380 -4569.700373 -317.624493 -4985.840766
## 267 268 269 270 271
## 9024.642500 -4133.801847 4377.236072 -1966.957031 4591.300412
## 272 273 274 275 276
## 822.128495 7411.437622 -1374.299284 12087.975719 -4636.130456
## 277 278 279 280 281
## 1740.250578 -361.738772 7879.698527 -5097.455661 -2698.681118
## 282 283 284 285 286
## -11187.897362 -2471.023747 18874.407566 7800.289723 2683.612141
## 287 288 289 290 291
## -686.081384 877.434735 6379.195183 6814.364437 -18888.689397
## 292 293 294 295 296
## -11025.627750 -7884.406933 9979.591920 3265.989211 -1022.543892
## 297 298 299 300 301
## 27570.512022 9938.058216 4697.477323 9303.141429 2582.408915
## 302 303 304 305 306
## -1287.144610 7700.880711 -24534.624177 -3462.367514 -52.657934
## 307 308 309 310 311
## -6837.800221 -3760.274753 3183.054340 -8982.057070 -2922.602777
## 312 313 314 315 316
## -7857.482699 1967.678519 -2791.877485 2420.971761 -3756.119679
## 317 318 319 320 321
## 27798.047029 -717.603459 3321.404725 10837.285368 5491.976627
## 322 323 324 325 326
## 32250.467425 4656.633423 -21372.793375 1662.779412 996.533583
## 327 328 329 330 331
## -6557.490885 -1722.940991 -33217.507077 1356.204447 -1865.129583
## 332 333 334 335 336
## 346.657409 -2751.073698 4516.405484 -77.058401 -6603.718148
## 337 338 339 340 341
## -2702.992734 -1764.545172 -7251.051355 4344.148266 -955.488729
## 342 343 344 345 346
## -1329.681982 -588.147868 571.703262 853.600541 -1270.928922
## 347 348 349 350 351
## -9096.672607 -12768.072369 2876.877939 -3825.195594 -3144.076877
## 352 353 354 355 356
## -5458.593462 2304.991561 1876.934689 3194.738498 -3386.749457
## 357 358 359 360 361
## -114.883302 1060.197409 7366.020126 526.932924 201.683531
## 362 363 364 365 366
## 2817.710563 -2551.004876 -645.056851 -8503.463167 -4283.928536
## 367 368 369 370 371
## -5829.028659 -4510.525031 -6779.797328 5544.792694 801.817697
## 372 373 374 375 376
## 7518.933659 -7346.666497 -1890.711668 -3006.764909 -2064.259245
## 377 378 379 380 381
## -12046.635877 2440.985092 -10157.378394 6269.773485 9796.862524
## 382 383 384 385 386
## 3441.731012 -2136.046182 1887.899881 6996.807595 11572.837280
## 387 388 389 390 391
## -5779.202756 -5254.975557 21.735543 8743.612543 1889.137995
## 392 393 394 395 396
## 11284.080500 -9943.000952 2854.362602 768.181549 621.629832
## 397 398 399 400 401
## -589.194659 -478.624324 -14386.048465 8816.809523 -999.309750
## 402 403 404 405 406
## -1173.125141 7199.024120 -7803.676502 -1059.852967 -2279.661005
## 407 408 409 410 411
## -5538.107199 -2510.511266 -3545.906691 -8351.189740 6626.544407
## 412 413 414 415 416
## 2024.674704 -7030.059263 -7271.715968 14714.093795 4094.469740
## 417 418 419 420 421
## 4709.194109 -7880.699086 -4483.190196 -2285.267387 3157.774720
## 422 423 424 425 426
## -13722.303925 -2335.034016 -8633.946907 3564.106511 7451.373149
## 427 428 429 430 431
## 6930.072729 -3734.199639 -3822.851049 -4382.075318 -1404.348880
## 432 433 434 435 436
## -5323.556230 -6186.676007 -5452.670139 -854.617116 -329.411146
## 437 438 439 440 441
## -4481.670310 3104.489014 5293.399629 -4692.378142 -1748.652065
## 442 443 444 445 446
## 1990.022103 -3466.632172 3235.697611 -6235.217738 -11700.467159
## 447 448 449 450 451
## -3973.417688 10202.094791 -1633.623445 5157.469650 -5546.277405
## 452 453 454 455 456
## -738.229778 763.737532 3383.500376 -11963.966545 3815.057818
## 457 458 459 460 461
## -6322.444185 6965.348949 3349.858809 2794.407050 -3596.518891
## 462 463 464 465 466
## 2386.558969 252.938169 2049.376959 -290.383192 3587.070324
## 467 468 469 470 471
## -2444.881245 6036.294203 -6781.718570 -2708.825050 -1914.282329
## 472 473 474 475 476
## -4350.631334 3358.987411 8109.060229 -5809.384851 1769.601459
## 477
## -5916.488644
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17202.34 20078.03 24374.62 24089.76 26466.70 23772.64 24496.13 19680.49
## 10 11 12 13 14 15 16 17
## 19414.41 16730.55 17515.81 14212.44 14264.12 14935.43 16649.00 14952.20
## 18 19 20 21 22 23 24 25
## 15997.23 15364.49 22517.87 21592.70 21067.35 22976.29 22295.53 22954.61
## 26 27 28 29 30 31 32 33
## 24819.06 18686.63 20430.07 28345.89 28404.13 28073.77 25679.43 27095.92
## 34 35 36 37 38 39 40 41
## 30977.44 31329.36 32752.65 30238.32 34183.88 37424.09 34450.71 31229.66
## 42 43 44 45 46 47 48 49
## 30065.89 20551.07 28145.10 30605.99 31706.10 38612.69 38102.21 42811.09
## 50 51 52 53 54 55 56 57
## 47089.78 39714.57 34228.62 29201.48 22277.25 28630.25 25176.64 21437.16
## 58 59 60 61 62 63 64 65
## 25889.67 27161.83 27461.82 27877.76 23707.34 40464.78 42327.21 37525.56
## 66 67 68 69 70 71 72 73
## 41774.63 46739.51 57514.51 55508.39 40603.81 38084.69 41132.81 35375.88
## 74 75 76 77 78 79 80 81
## 30782.58 21392.92 24626.46 20511.37 22618.32 17462.74 19498.35 18717.13
## 82 83 84 85 86 87 88 89
## 17735.12 15784.72 17075.28 20719.47 25181.43 26181.21 26206.42 26829.33
## 90 91 92 93 94 95 96 97
## 30995.72 29814.56 30823.92 28868.98 28060.78 28432.61 28843.26 22356.40
## 98 99 100 101 102 103 104 105
## 25401.63 18362.32 17207.00 15228.11 15530.86 16215.03 20732.45 19806.78
## 106 107 108 109 110 111 112 113
## 23353.03 23140.90 24833.80 27739.37 25212.44 21620.31 21898.49 24571.25
## 114 115 116 117 118 119 120 121
## 35544.52 33719.28 35575.15 38603.49 40578.98 38244.26 33013.43 29318.20
## 122 123 124 125 126 127 128 129
## 31421.87 29684.61 30877.99 38553.83 38192.71 37244.86 34076.99 35876.02
## 130 131 132 133 134 135 136 137
## 41327.93 40762.62 31876.26 33153.72 36375.41 32750.13 31121.26 30197.10
## 138 139 140 141 142 143 144 145
## 26719.67 28148.26 27915.58 25578.72 27623.41 26234.40 19772.50 22833.35
## 146 147 148 149 150 151 152 153
## 20638.30 23645.20 24190.60 25796.90 25980.80 27651.52 28965.27 32027.77
## 154 155 156 157 158 159 160 161
## 27452.62 26708.20 24239.00 30192.06 41152.24 39465.77 36804.34 41874.85
## 162 163 164 165 166 167 168 169
## 43297.51 46747.58 42168.15 37428.39 42907.90 59373.17 61607.43 60005.66
## 170 171 172 173 174 175 176 177
## 56799.60 55182.83 57912.44 56926.78 49141.79 51993.54 55773.60 55810.15
## 178 179 180 181 182 183 184 185
## 63010.30 53417.59 50136.03 40852.75 32307.47 35850.82 46055.35 45505.92
## 186 187 188 189 190 191 192 193
## 51518.23 57320.67 68211.51 73547.21 67178.83 67374.20 74515.22 70148.43
## 194 195 196 197 198 199 200 201
## 65626.47 54762.73 48733.16 50171.70 45722.34 37812.82 44301.83 42511.49
## 202 203 204 205 206 207 208 209
## 42145.84 42628.97 49490.93 58469.06 58094.96 59837.01 61509.36 65338.46
## 210 211 212 213 214 215 216 217
## 74899.66 66903.30 54974.23 49529.13 40435.42 37362.72 40507.52 30427.90
## 218 219 220 221 222 223 224 225
## 47571.16 54965.16 55876.08 78869.17 86439.12 88462.38 96129.64 86990.06
## 226 227 228 229 230 231 232 233
## 80928.47 80506.23 77122.70 76275.81 81060.19 82438.10 76817.92 71983.00
## 234 235 236 237 238 239 240 241
## 77693.17 64207.46 56164.34 48034.49 39562.19 43796.01 45970.27 39355.48
## 242 243 244 245 246 247 248 249
## 32971.85 43357.12 37546.49 41521.54 33708.07 32400.99 36092.83 38944.95
## 250 251 252 253 254 255 256 257
## 29682.73 35680.16 39519.59 44766.30 47511.79 46999.29 57403.23 75075.36
## 258 259 260 261 262 263 264 265
## 74888.57 68132.35 69629.00 65812.01 67272.60 60967.88 50135.27 46122.91
## 266 267 268 269 270 271 272 273
## 46334.41 42402.21 51294.37 47530.19 51718.39 49816.13 53924.16 54223.13
## 274 275 276 277 278 279 280 281
## 60300.73 57911.31 67680.99 61545.04 61757.17 60089.73 65890.03 59557.82
## 282 283 284 285 286 287 288 289
## 56087.33 45535.17 43915.88 61320.42 66905.82 67319.37 64711.14 63789.38
## 290 291 292 293 294 295 296 297
## 67830.35 71779.69 52586.20 42589.26 36540.41 46965.01 50239.26 49344.35
## 298 299 300 301 302 303 304 305
## 73782.66 79787.52 80461.86 85120.45 83301.00 78281.55 81783.05 56430.80
## 306 307 308 309 310 311 312 313
## 52654.52 52331.09 46059.13 43240.66 46880.06 39357.75 38067.05 32574.18
## 314 315 316 317 318 319 320 321
## 36396.59 35569.74 39439.55 37403.81 63448.17 61267.74 62907.57 70985.74
## 322 323 324 325 326 327 328 329
## 73396.96 99133.65 97495.08 73083.36 71869.18 70210.06 62081.23 59174.65
## 330 331 332 333 334 335 336 337
## 28822.22 32546.70 32990.63 35333.79 34668.02 40492.77 41579.15 36779.14
## 338 339 340 341 342 343 344 345
## 35985.69 36113.62 31385.71 37444.77 38114.82 38375.86 39260.44 41064.26
## 346 347 348 349 350 351 352 353
## 42904.50 42653.67 35527.64 26000.98 31399.20 30248.79 29834.74 27427.29
## 354 355 356 357 358 359 360 361
## 32153.07 35944.98 40453.32 38624.17 39897.09 42056.98 49526.35 50082.46
## 362 363 364 365 366 367 368 369
## 50286.15 52774.00 50232.20 49671.18 42242.64 39411.31 35549.95 33306.37
## 370 371 372 373 374 375 376 377
## 29324.64 36685.61 38995.49 46960.10 40871.28 40312.91 38835.54 38363.64
## 378 379 380 381 382 383 384 385
## 29139.73 33783.95 26765.94 35067.71 45504.41 49105.62 47361.67 49373.34
## 386 387 388 389 390 391 392 393
## 55655.88 65236.49 58379.69 52792.41 52518.39 59972.00 60500.63 69256.29
## 394 395 396 397 398 399 400 401
## 58252.64 59835.25 59390.94 58869.62 57341.34 56090.48 42716.19 51388.02
## 402 403 404 405 406 407 408 409
## 50378.41 49334.26 55799.82 48267.42 47571.66 45881.54 41515.37 40334.34
## 410 411 412 413 414 415 416 417
## 38378.76 32413.60 40365.47 43321.20 37940.00 32978.91 47999.96 51883.38
## 418 419 420 421 422 423 424 425
## 55852.13 48245.62 44531.98 43194.65 46817.16 35119.89 34846.38 29047.46
## 426 427 428 429 430 431 432 433
## 34693.48 43104.78 50066.20 46799.14 43838.36 40732.63 40619.70 37062.10
## 434 435 436 437 438 439 440 441
## 33161.67 30367.90 31959.84 33827.81 31812.37 36727.46 42995.38 39715.08
## 442 443 444 445 446 447 448 449
## 39418.12 42454.78 40319.59 44349.22 39548.32 30490.42 29316.19 40787.34
## 450 451 452 453 454 455 456 457
## 40465.67 46173.71 41765.94 42119.12 43755.93 47511.54 37283.94 42182.02
## 458 459 460 461 462 463 464 465
## 37559.22 45204.43 48759.88 51406.80 48103.44 50467.78 50671.34 52435.95
## 466 467 468 469 470 471 472 473
## 51928.50 54901.88 52203.28 57305.29 50497.40 48084.28 46656.20 43246.58
## 474 475 476 477
## 47040.51 54578.96 48949.83 50670.20
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8572
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.737916 0.5821703 2.997483
## t2* 1648.101007 29.6143243 245.336621
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.29647 4.850437 10.8872
## 2 lag_depvar 1307.05441 1658.965599 2113.1083Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Aug 01 00:53:11 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 01 00:53:18 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Aug 01 00:53:25 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Aug 01 00:53:32 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Aug 01 00:53:39 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 01 00:53:46 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 01 00:53:53 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Aug 01 00:54:00 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Aug 01 00:54:07 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Aug 01 00:54:14 2022
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3) %>%
knitr::kable(format="html", caption="Tabla. Gastos promedio por ítem a contar del...",
col.names= c("Item","2023","2022","2021","2020")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | NA | 6.281714 | 6.008526 | 7.529516 |
| Comida | NA | 300.780286 | 311.590579 | 343.078226 |
| Comunicaciones | NA | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | NA | 43.067143 | 34.512947 | 29.129064 |
| Enceres | NA | 14.113571 | 14.547842 | 24.017839 |
| Farmacia | NA | 3.140000 | 9.996474 | 11.560452 |
| Gas/Bencina | NA | 61.750000 | 31.329158 | 25.882387 |
| Diosi | NA | 19.003857 | 40.277947 | 39.056032 |
| donaciones/regalos | NA | 0.000000 | 9.056947 | 8.861903 |
| Electrodomésticos/ Mantención casa | NA | 6.761143 | 38.235158 | 26.757032 |
| VTR | NA | 27.990000 | 22.367000 | 21.107677 |
| Netflix | NA | 7.505286 | 7.204316 | 7.608032 |
| Otros | NA | 5.401857 | 1.990158 | 1.219774 |
| Total | 0 | 495.794857 | 527.117053 | 545.807935 |
## Joining, by = "word"
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: 35 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1682, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2022-08-09 00:04:58 sería de: 34.530 pesos// Percentil 95% más alto proyectado: 37.582,14
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="html", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 33599.83 | 33568.79 |
| Lo.80 | 33689.29 | 33661.54 |
| Point.Forecast | 34529.69 | 36388.94 |
| Hi.80 | 36218.05 | 41084.90 |
| Hi.95 | 37144.98 | 43570.79 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3280 985.7562
## s.e. 0.1535 38.4750
##
## sigma^2 = 29191: log likelihood = -267.98
## AIC=541.96 AICc=542.61 BIC=547.1paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 xreg
## 0.3618 33.3818
## s.e. 0.1548 1.3979
##
## sigma^2 = 30136: log likelihood = -268.65
## AIC=543.3 AICc=543.94 BIC=548.44forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="html", caption="Tabla. Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 875.1395 | 631.2725 | 664.1008 |
| Lo.80 | 1001.4686 | 753.9717 | 743.6058 |
| Point.Forecast | 1240.1098 | 985.7558 | 920.6852 |
| Hi.80 | 1478.7510 | 1217.5400 | 1219.2415 |
| Hi.95 | 1605.0801 | 1340.2391 | 1414.6897 |
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.2.7 bsts_0.9.8 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.10 MASS_7.3-54 scales_1.2.0
## [7] ggiraph_0.8.2 tidytext_0.3.3 DT_0.23
## [10] autoplotly_0.1.4 rvest_1.0.2 plotly_4.10.0
## [13] xts_0.12.1 forecast_8.17.0 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.0 tm_0.7-8
## [19] NLP_0.2-1 tsibble_1.1.1 forcats_0.5.1
## [22] dplyr_1.0.9 purrr_0.3.4 tidyr_1.2.0
## [25] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
## [28] sjPlot_2.8.10 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.7.7 httr_1.4.3
## [34] readxl_1.4.0 zoo_1.8-10 stringr_1.4.0
## [37] stringi_1.7.8 DataExplorer_0.8.2 data.table_1.14.2
## [40] reshape2_1.4.4 fUnitRoots_3042.79 fBasics_3042.89.2
## [43] timeSeries_4021.104 timeDate_4021.104 plyr_1.8.7
## [46] readr_2.1.2
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.2 lme4_1.1-30
## [4] htmlwidgets_1.5.4 munsell_0.5.0 codetools_0.2-18
## [7] effectsize_0.7.0 its.analysis_1.6.0 withr_2.5.0
## [10] colorspace_2.0-3 ggfortify_0.4.14 highr_0.9
## [13] knitr_1.39 uuid_1.1-0 rstudioapi_0.13
## [16] TTR_0.24.3 labeling_0.4.2 emmeans_1.7.5
## [19] slam_0.1-50 bit64_4.0.5 farver_2.1.1
## [22] datawizard_0.4.1 rprojroot_2.0.3 vctrs_0.4.1
## [25] generics_0.1.3 xfun_0.31 R6_2.5.1
## [28] bitops_1.0-7 cachem_1.0.6 assertthat_0.2.1
## [31] networkD3_0.4 vroom_1.5.7 nnet_7.3-16
## [34] googlesheets4_1.0.0 gtable_0.3.0 spatial_7.3-14
## [37] rlang_1.0.4 forge_0.2.0 systemfonts_1.0.4
## [40] splines_4.1.2 lazyeval_0.2.2 gargle_1.2.0
## [43] selectr_0.4-2 broom_1.0.0 yaml_2.3.5
## [46] abind_1.4-5 modelr_0.1.8 crosstalk_1.2.0
## [49] backports_1.4.1 quantmod_0.4.20 tokenizers_0.2.1
## [52] tools_4.1.2 ellipsis_0.3.2 gplots_3.1.3
## [55] kableExtra_1.3.4 jquerylib_0.1.4 Rcpp_1.0.9
## [58] base64enc_0.1-3 fracdiff_1.5-1 haven_2.5.0
## [61] fs_1.5.2 magrittr_2.0.3 lmtest_0.9-40
## [64] reprex_2.0.1 googledrive_2.0.0 mvtnorm_1.1-3
## [67] sjmisc_2.8.9 hms_1.1.1 evaluate_0.15
## [70] xtable_1.8-4 sjstats_0.18.1 ggeffects_1.1.2
## [73] compiler_4.1.2 KernSmooth_2.23-20 crayon_1.5.1
## [76] minqa_1.2.4 htmltools_0.5.3 tzdb_0.3.0
## [79] lubridate_1.8.0 DBI_1.1.3 sjlabelled_1.2.0
## [82] dbplyr_2.2.1 boot_1.3-28 Matrix_1.3-4
## [85] car_3.1-0 cli_3.3.0 quadprog_1.5-8
## [88] parallel_4.1.2 insight_0.18.0 igraph_1.3.4
## [91] pkgconfig_2.0.3 xml2_1.3.3 svglite_2.1.0
## [94] bslib_0.4.0 webshot_0.5.3 estimability_1.4
## [97] anytime_0.3.9 snakecase_0.11.0 janeaustenr_0.1.5
## [100] digest_0.6.29 parameters_0.18.1 janitor_2.1.0
## [103] rmarkdown_2.14 cellranger_1.1.0 curl_4.3.2
## [106] gtools_3.9.3 urca_1.3-0 nloptr_2.0.3
## [109] lifecycle_1.0.1 nlme_3.1-153 jsonlite_1.8.0
## [112] tseries_0.10-51 carData_3.0-5 viridisLite_0.4.0
## [115] fansi_1.0.3 pillar_1.8.0 fastmap_1.1.0
## [118] glue_1.6.2 bayestestR_0.12.1 bit_4.0.4
## [121] sass_0.4.2 performance_0.9.1 r2d3_0.2.6
## [124] caTools_1.18.2#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Paquetes estadísticos utilizados')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({'font-size': '80%'});",
"}")))